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Abstract This work aims to predict the general performances of a pilot parabolic trough collector during transient periods. To do so, a one-dimensional thermal model has been developed. It has been validated with experimental results from two different experimental setups, in steady-state conditions, with a transmitted power maximum error of 3.4%. Since the model only predicts the collector's thermal behavior, the parabolic trough collector has been first optically qualified. Then, optical efficiencies were used as input for the model. Experimental results were obtained in steady-state conditions and compared to the model. Then, experimental and numerical results were compared during two period of time with varying inlet conditions (i.e. dynamic condition tests): the first one with stable conditions, and the other one with harsh conditions. The developed model showed a good capability of predicting the thermal behavior of the parabolic trough collector with unstable environment (DNI, mass flow, inlet temperature), with a 9.6% relative standard error in the worst case. As a conclusion, while previous studies only focused on steady-state conditions, it has been showed that this kind of model can be used to precisely predict the dynamic behavior of large power plants.

This paper deals the study of the lead-acid behavior and its modelling based on the Kinetic Battery Model. Results of experimental tests are used to realize the analysis and to estimate the model parameters. These last can serve for prediction and estimation of the battery lifetime according to the actual operating conditions.

Abstract Building-based active envelopes play an important role to reduce active heating supplies. Several techniques are developed to enhance the energy performances of active building envelopes; meanwhile, numerous numerical and mathematical models are also developed to conduct the performance analysis of these techniques. In this paper, we propose a state-space model for solar active wall-based Phase Change Materials (PCM). The advantage of this method remains in its simplicity to provide details of internal nodes and input/output parameters. The low-cost calculation is a supplementary advantage versus a heavy numerical method. The proposed numerical model is applied for a multi-layer wall with PCM Wallboards (PCMW) embedded between indoor and outdoor environments. The results show the ability of the state-space model to estimate the thermal behavior of the system, as well as the thermal characteristics of embedding PCM in the internal face of the wall. It significantly contributes to stabilize the indoor temperature and to ensure the thermal comfort.

Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view of the supplier play a role in load balancing, but do not lead to optimal demand patterns. In the context of charging fleets of electric vehicles, we propose a centralised method for setting overnight charging schedules. This method uses evolutionary algorithms to automatically search for optimal plans, representing both the charging schedule and the energy drawn from the grid at each time-step. In successive experiments, we optimise for increased state of charge, reduced peak demand, and reduced consumer costs. In simulations, the centralised method achieves improvements in performance relative to simple models of non-centralised consumer behaviour.

This paper presents an experimental study of indoor thermal environment near a full-scale glass facade with different types of shading devices under varying climatic conditions in winter. Interior glazing and shading temperature, operative temperature and radiant temperature asymmetry were measured for facade sections with roller shades and venetian blinds at different positions. Interior glass surface temperatures can be high during sunny days with low outdoor temperature. Shading systems significantly improved operative temperature and radiant temperature asymmetry during cold sunny days, depending on their properties and tilt angle. During cloudy days the impact was smaller, however the shading layers could still decrease the amount of heat loss through the facade. A transient building thermal model, which also calculates indoor environmental indices under the presence of solar radiation, was developed and compared with the experimental measurements. Part II of this paper uses this validated model with a transient, two-node thermal comfort model (including transmitted solar radiation) for assessment of indoor environmental conditions with different building envelope and shading properties, facade location and orientation.

This paper presents the direct power control (DPC) of asymmetrical six-phase permanent magnet synchronous generator (A6PMSG) which supplied the isolated load via two PWM rectifiers. The study concerns the method of control by the converter (DPC). The proposed method is around two hysteresis controllers that enable the adjustment of active and reactive power. The research in this paper is verified by MATLAB SIMULINK software. It has been demonstrated through the simulation results that the proposed strategy can be an attractive and practical solution to multi-phase machines applications.

Abstract The hybrid pneumatic power system (HPPS) proposed in this research replaces the battery’s electric-chemical energy with flow work and optimizes the management and utilization of the energy. This power system is able to keep the internal-combustion engine working at its optimal condition and turn its waste energy into effective mechanical energy and so enhance the thermal efficiency of the whole system. Using computer simulation software ITI-SIM, this study simulates the overall dynamic characteristics of the system in accordance with the regulated running-vehicle test-mode ECE47, and, with experimental verification and analysis, proves that this system can meet the requirements of the standard running-car mode. As for recycling the waste energy, the experimental results show that this design could offset the shortcomings of the low-density of pneumatic power and so effectively enhance the efficiency of the whole system.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

In this paper, fault prognosis of aircraft jet engines are considered using computationally intelligent-based methodologies to ensure flight safety and performance. Two different dynamic neural networks namely, the nonlinear autoregressive neural networks with exogenous input (NARX) and the Elman neural networks are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the jet engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine is then predicted subject to occurrence of these deteriorations. In both proposed approaches, two scenarios are considered. For each scenario, several neural networks are trained and their performance in predicting multi-flights ahead turbine output temperature are evaluated. Finally, the most suitable neural network for prediction is selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.